{"pageNumber":"429","pageRowStart":"10700","pageSize":"25","recordCount":165990,"records":[{"id":70227422,"text":"70227422 - 2022 - Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","interactions":[],"lastModifiedDate":"2022-01-14T15:32:34.362133","indexId":"70227422","displayToPublicDate":"2021-12-31T09:21:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9944,"text":"Remote Sensing of the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics","docAbstract":"<p><span>Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with&nbsp;satellite remote sensing&nbsp;across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies&nbsp;normalized difference vegetation index&nbsp;(NDVI), soil adjusted&nbsp;vegetation index&nbsp;(SAVI),&nbsp;near infrared&nbsp;reflectance index (NIR</span><sub>v</sub><span>), and kernel NDVI (kNDVI) from the&nbsp;MODerate resolution Imaging Spectroradiometer&nbsp;(MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States&nbsp;eddy covariance&nbsp;tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR</span><sub>v</sub><span>&nbsp;and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR</span><sub>v</sub><span>&nbsp;offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.59&nbsp;±&nbsp;0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.74&nbsp;±&nbsp;0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIR</span><sub>v</sub><span>&nbsp;and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIR</span><sub>v</sub><span>&nbsp;and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112858","usgsCitation":"Wang, X., Biederman, J.A., Knowles, J.F., Scott, R.L., Turner, A.J., Dannenberg, M.P., Kohler, P., Frankenberg, C., Litvak, M.E., Flerchinger, G.N., Law, B.E., Kwon, H., Reed, S., Parton, W.J., Barron-Gafford, G.A., and Smith, W.K., 2022, Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics: Remote Sensing of the Environment, v. 270, 112858, 11 p., https://doi.org/10.1016/j.rse.2021.112858.","productDescription":"112858, 11 p.","ipdsId":"IP-133234","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1981744","text":"Publisher Index Page"},{"id":394380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, New Mexico, Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -106.083984375,\n              38.94232097947902\n            ],\n            [\n              -104.1064453125,\n              38.94232097947902\n            ],\n            [\n              -104.1064453125,\n              40.94671366508002\n            ],\n            [\n              -106.083984375,\n              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A.","contributorId":201939,"corporation":false,"usgs":false,"family":"Biederman","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":830797,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knowles, John F.","contributorId":203853,"corporation":false,"usgs":false,"family":"Knowles","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":830798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Russell L.","contributorId":39875,"corporation":false,"usgs":false,"family":"Scott","given":"Russell","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":830799,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Alexander J","contributorId":271092,"corporation":false,"usgs":false,"family":"Turner","given":"Alexander","email":"","middleInitial":"J","affiliations":[{"id":56276,"text":"Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA","active":true,"usgs":false}],"preferred":false,"id":830800,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dannenberg, Matthew P.","contributorId":239668,"corporation":false,"usgs":false,"family":"Dannenberg","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":47960,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ; Geographical and Sustainability Services, University of Iowa, Iowa City, IA","active":true,"usgs":false}],"preferred":false,"id":830801,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kohler, Philipp","contributorId":271093,"corporation":false,"usgs":false,"family":"Kohler","given":"Philipp","email":"","affiliations":[{"id":33000,"text":"Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":830802,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frankenberg, Christian","contributorId":271094,"corporation":false,"usgs":false,"family":"Frankenberg","given":"Christian","email":"","affiliations":[{"id":33000,"text":"Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":830803,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Litvak, Marcy E","contributorId":271095,"corporation":false,"usgs":false,"family":"Litvak","given":"Marcy","email":"","middleInitial":"E","affiliations":[{"id":34162,"text":"Department of Biology, University of New Mexico, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":830804,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flerchinger, Gerald N.","contributorId":257377,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","email":"","middleInitial":"N.","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":830805,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Law, Beverly E.","contributorId":222527,"corporation":false,"usgs":false,"family":"Law","given":"Beverly","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":830806,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kwon, Hyojung","contributorId":271096,"corporation":false,"usgs":false,"family":"Kwon","given":"Hyojung","email":"","affiliations":[{"id":56277,"text":"Department of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, USA","active":true,"usgs":false}],"preferred":false,"id":830807,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830808,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parton, William J","contributorId":271097,"corporation":false,"usgs":false,"family":"Parton","given":"William","email":"","middleInitial":"J","affiliations":[{"id":16129,"text":"Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA","active":true,"usgs":false}],"preferred":false,"id":830809,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Barron-Gafford, Greg A.","contributorId":19058,"corporation":false,"usgs":false,"family":"Barron-Gafford","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":830810,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Smith, William K. 0000-0002-5785-6489","orcid":"https://orcid.org/0000-0002-5785-6489","contributorId":239667,"corporation":false,"usgs":false,"family":"Smith","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":830811,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}}
,{"id":70227508,"text":"70227508 - 2022 - Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","interactions":[],"lastModifiedDate":"2022-01-20T13:30:49.473216","indexId":"70227508","displayToPublicDate":"2021-12-31T07:30:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0045\">Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O<sub>2</sub>&nbsp;&lt;&nbsp;3&nbsp;mg&nbsp;L<sup>−1</sup>) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O<sub>2</sub>&nbsp;&lt;&nbsp;1&nbsp;mg&nbsp;L<sup>−1</sup>, where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.152722","usgsCitation":"Frankel, L.T., Friedrichs, M.A., St-Laurent, P., Bever, A.J., Lipcius, R.N., Bhatt, G., and Shenk, G.W., 2022, Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling: Science of the Total Environment, v. 814, 152722, 17 p., https://doi.org/10.1016/j.scitotenv.2021.152722.","productDescription":"152722, 17 p.","ipdsId":"IP-135162","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":449327,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2021.152722","text":"Publisher Index Page"},{"id":394573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -78.50830078125001,\n              35.72867704485167\n            ],\n            [\n              -74.794921875,\n              35.72867704485167\n            ],\n            [\n              -74.794921875,\n              40.94671366507999\n            ],\n            [\n              -78.50830078125001,\n              40.94671366507999\n            ],\n            [\n              -78.50830078125001,\n              35.72867704485167\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"814","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Frankel, Luke T 0000-0001-9690-2671","orcid":"https://orcid.org/0000-0001-9690-2671","contributorId":271212,"corporation":false,"usgs":false,"family":"Frankel","given":"Luke","email":"","middleInitial":"T","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":831198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedrichs, Marjorie A. M. 0000-0003-2828-7595","orcid":"https://orcid.org/0000-0003-2828-7595","contributorId":222588,"corporation":false,"usgs":false,"family":"Friedrichs","given":"Marjorie","email":"","middleInitial":"A. M.","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":831199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"St-Laurent, Pierre 0000-0002-1700-9509","orcid":"https://orcid.org/0000-0002-1700-9509","contributorId":261288,"corporation":false,"usgs":false,"family":"St-Laurent","given":"Pierre","email":"","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":831200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bever, Aaron J.","contributorId":173009,"corporation":false,"usgs":false,"family":"Bever","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":27140,"text":"Delta Modeling Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":831201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lipcius, Romuald N.","contributorId":101451,"corporation":false,"usgs":false,"family":"Lipcius","given":"Romuald","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":831202,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":831203,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831204,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70229111,"text":"70229111 - 2022 - Acute and lagged fitness consequences for a sagebrush obligate in a post mega-wildfire landscape","interactions":[],"lastModifiedDate":"2022-03-02T12:06:50.746406","indexId":"70229111","displayToPublicDate":"2021-12-30T18:24:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Acute and lagged fitness consequences for a sagebrush obligate in a post mega-wildfire landscape","docAbstract":"<div class=\"textLayer\"><span dir=\"ltr\">Species responses to disturbance influence their extinction risks. Greater sage- </span><span dir=\"ltr\">grouse </span><span dir=\"ltr\">(</span><span dir=\"ltr\">Centrocercus urophasianus</span><span dir=\"ltr\">) are bioindicators of sagebrush ecosystem health and the </span><span dir=\"ltr\">loss of sagebrush (</span><span dir=\"ltr\">Artemisia</span><span dir=\"ltr\"> spp.) due to wildfire, can cause long-</span><span dir=\"ltr\">term declines in </span><span dir=\"ltr\">sage- </span><span dir=\"ltr\">grouse populations and other sagebrush obligate species. We examined the de</span><span dir=\"ltr\">-</span><span dir=\"ltr\">mographic response of a greater sage- </span><span dir=\"ltr\">grouse population following a mega-</span><span dir=\"ltr\">wildfire </span><span dir=\"ltr\">using stochastic age-</span><span dir=\"ltr\">structured female- </span><span dir=\"ltr\">based matrix models over 6 years (2013– </span><span dir=\"ltr\">2018). Notably, chick survival (range </span><span dir=\"ltr\">=</span><span dir=\"ltr\"> 0.18–</span><span dir=\"ltr\">0.38) and female survival (yearling range: </span><span dir=\"ltr\">0.20–</span><span dir=\"ltr\">0.68; adult range: 0.27–</span><span dir=\"ltr\">0.75) were low compared to values reported for greater </span><span dir=\"ltr\">sage- </span><span dir=\"ltr\">grouse in other parts of their distribution. Greater sage- </span><span dir=\"ltr\">grouse displayed vari</span><span dir=\"ltr\">-</span><span dir=\"ltr\">ation in demographic tactics after the fire; however, adult female survival explained </span><span dir=\"ltr\">most of the variation in </span><span dir=\"ltr\">λ</span><span dir=\"ltr\"> during each year, which reflected a declining population in </span><span dir=\"ltr\">3 of 6 years with more uncertainty observed in 2015 when populations may have </span><span dir=\"ltr\">been increasing, and 2017 and 2018, when populations may have been declining. The </span><span dir=\"ltr\">continued annual population decline observed since 2016 suggested there were ad</span><span dir=\"ltr\">-</span><span dir=\"ltr\">ditional strong environmental impacts that may have been compounded by the fire </span><span dir=\"ltr\">effects, </span><span dir=\"ltr\">prolonging </span><span dir=\"ltr\">recovery </span><span dir=\"ltr\">of greater </span><span dir=\"ltr\">sage- </span><span dir=\"ltr\">grouse. </span><span dir=\"ltr\">Our </span><span dir=\"ltr\">results </span><span dir=\"ltr\">support </span><span dir=\"ltr\">others </span><span dir=\"ltr\">that </span><span dir=\"ltr\">reported negative effects to greater sage- </span><span dir=\"ltr\">grouse demographics from broad-</span><span dir=\"ltr\">scale fire </span><span dir=\"ltr\">and provide a baseline for understanding how this species responds to loss of sage</span><span dir=\"ltr\">-</span><span dir=\"ltr\">brush cover based on their life history strategy.</span></div>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8488","usgsCitation":"Anthony, C., Foster, L.J., Hagen, C., and Dugger, K., 2022, Acute and lagged fitness consequences for a sagebrush obligate in a post mega-wildfire landscape: Ecology and Evolution, v. 12, e8488, 12 p., https://doi.org/10.1002/ece3.8488.","productDescription":"e8488, 12 p.","ipdsId":"IP-122398","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":449329,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8488","text":"External Repository"},{"id":396616,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Oregon","otherGeospatial":"Trout Creek Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -118.76220703125001,\n              41.16211393939692\n            ],\n            [\n              -117.04833984375001,\n              41.16211393939692\n            ],\n            [\n              -117.04833984375001,\n              42.924251753870685\n            ],\n            [\n              -118.76220703125001,\n              42.924251753870685\n            ],\n            [\n              -118.76220703125001,\n              41.16211393939692\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Anthony, Christopher R.","contributorId":287179,"corporation":false,"usgs":false,"family":"Anthony","given":"Christopher R.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":836546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Lee J.","contributorId":287180,"corporation":false,"usgs":false,"family":"Foster","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":836547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagen, Christian A.","contributorId":287181,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":836548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836545,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227141,"text":"70227141 - 2022 - Seasonal variability of invertebrate prey diet and selectivity of the dominant forage fishes in Lake Huron","interactions":[],"lastModifiedDate":"2022-01-03T14:54:41.534627","indexId":"70227141","displayToPublicDate":"2021-12-30T14:19:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5453,"text":"Food Webs","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal variability of invertebrate prey diet and selectivity of the dominant forage fishes in Lake Huron","docAbstract":"<p><span>Predators can shift their diets and even selectivity in response to changing environmental conditions. Since the early 2000s, Lake Huron experienced major food-web shifts that have caused changes in the prey availability and quality for consumers at multiple&nbsp;trophic levels. Previous studies have reported declining energetic condition for key planktivorous fishes, such as bloater (</span><span><i>Coregonus</i><i>&nbsp;hoyi</i></span><span>) and rainbow smelt (</span><span><i>Osmerus mordax</i></span><span>), which play a key role in supporting commercially and recreationally important piscivorous fishes. To improve understanding of how changes in the invertebrate prey community have influenced foraging by rainbow smelt and bloater, we processed diets and calculated selectivity along two transects in Lake Huron during 2012. Diet proportions for both species varied seasonally and consisted of mostly calanoid copepods during spring and summer, specifically&nbsp;</span><i>Leptodiaptomus sicilis</i><span>&nbsp;and diaptomid copepodites, and&nbsp;</span><span><i>Daphnia galeata</i><i>&nbsp;mendotae</i></span><span>&nbsp;in autumn. Bloater selectivity varied primarily by season and transect with&nbsp;</span><i>Limnocalanus macrurus</i><span>&nbsp;or&nbsp;</span><span><i>Mysis</i><i>&nbsp;diluviana</i></span><span>&nbsp;as the most preferred prey during spring,&nbsp;Chironomidae&nbsp;pupae&nbsp;or L.&nbsp;</span><i>sicilis</i><span>&nbsp;during summer, and Chironomidae pupae or&nbsp;</span><i>Mysis</i><span>&nbsp;during autumn. Rainbow smelt selectivity was consistent across seasons and transects with&nbsp;</span><i>Mysis</i><span>&nbsp;being the species most commonly selected and&nbsp;</span><span><i>Bythotrephes longimanus</i></span><span>&nbsp;the second most. Both fish species selected for relatively large invertebrate prey, but declining densities of&nbsp;</span><i>Mysis</i><span>&nbsp;and the benthic amphipod&nbsp;</span><i>Diporeia</i><span>&nbsp;have caused fish to consume smaller prey with much lower energy density. Our results illustrate how food-web changes underlie the reduced energetic condition of bloater and rainbow smelt, which ultimately reduces the growth potential for recreationally and commercially important piscivorous fish.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2021.e00215","usgsCitation":"Dieter, P.M., Bunnell, D., and Warner, D.M., 2022, Seasonal variability of invertebrate prey diet and selectivity of the dominant forage fishes in Lake Huron: Food Webs, v. 30, p. 1-10, https://doi.org/10.1016/j.fooweb.2021.e00215.","productDescription":"e00215, 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-128401","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":436020,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LFPAMF","text":"USGS data release","linkHelpText":"Zooplankton, Benthos, Mysis, and fish diet data from northern Lake Huron in 2012"},{"id":393700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Huron","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.0673828125,\n              46.34692761055676\n            ],\n            [\n              -84.72656249999999,\n              46.057985244793024\n            ],\n            [\n              -84.72656249999999,\n              45.79816953017265\n            ],\n            [\n              -83.69384765625,\n              43.5326204268101\n            ],\n            [\n              -82.37548828125,\n              43.052833917627936\n            ],\n            [\n              -81.67236328125,\n              43.42100882994726\n            ],\n            [\n              -80.0244140625,\n              44.62175409623324\n            ],\n            [\n              -79.8046875,\n              44.824708282300236\n            ],\n            [\n              -80.70556640625,\n              46.057985244793024\n            ],\n            [\n              -84.0673828125,\n              46.34692761055676\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dieter, Patricia M 0000-0003-1686-2679 pdieter@usgs.gov","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":270688,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","email":"pdieter@usgs.gov","middleInitial":"M","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829766,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":245523,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":829768,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227176,"text":"70227176 - 2022 - Food, beverage, and feedstock processing facility wastewater: A unique and underappreciated source of contaminants to U.S. streams","interactions":[],"lastModifiedDate":"2022-02-15T16:19:12.225859","indexId":"70227176","displayToPublicDate":"2021-12-30T08:50:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Food, beverage, and feedstock processing facility wastewater: A unique and underappreciated source of contaminants to U.S. streams","docAbstract":"<p>Process wastewaters from food, beverage, and feedstock facilities, although regulated, are an under-investigated environmental contaminant source. Food process wastewaters (FPWWs) from 23 facilities in 17 U.S. states were sampled and documented for a plethora of chemical and microbial contaminants. Of the 576 analyzed organics, 184 (32%) were detected at least once, with concentrations as large as 143 μg L<sup>–1</sup> (6:2 fluorotelomer sulfonic acid), and as many as 47 were detected in a single FPWW sample. Cumulative per/polyfluoroalkyl substance concentrations up to 185 μg L<sup>–1</sup> and large pesticide transformation product concentrations (e.g., methomyl oxime, 40 μg L<sup>–1</sup>; clothianidin TMG, 2.02 μg L<sup>–1</sup>) were observed. Despite 48% of FPWW undergoing disinfection treatment prior to discharge, bacteria resistant to third-generation antibiotics were found in each facility type, and multiple bacterial groups were detected in all samples, including total coliforms. The exposure–activity ratios and toxicity quotients exceeded 1.0 in 13 and 22% of samples, respectively, indicating potential biological effects and toxicity to vertebrates and invertebrates associated with the discharge of FPWW. Organic contaminant profiles of FPWW differed from previously reported contaminant profiles of municipal effluents and urban storm water, indicating that FPWW is another important source of chemical and microbial contaminant mixtures discharged into receiving surface waters.</p>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c06821","usgsCitation":"Hubbard, L.E., Kolpin, D., Givens, C.E., Blackwell, B.D., Bradley, P., Gray, J., Lane, R.F., Masoner, J.R., McCleskey, R., Romanok, K., Sandstrom, M.W., Smalling, K., and Villeneuve, D., 2022, Food, beverage, and feedstock processing facility wastewater: A unique and underappreciated source of contaminants to U.S. streams: Environmental Science & Technology, v. 56, no. 2, p. 1028-1040, https://doi.org/10.1021/acs.est.1c06821.","productDescription":"13 p.","startPage":"1028","endPage":"1040","ipdsId":"IP-130265","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449332,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9219000","text":"External Repository"},{"id":436021,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WJYI2H","text":"USGS data release","linkHelpText":"Concentrations of inorganic, organic, and microbial analytes from a national reconnaissance of wastewater from food, beverage, and feedstock 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"properties\":{\"name\":\"Alabama\",\"nation\":\"USA 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Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829922,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":270741,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blackwell, Bradley D. 0000-0003-1296-4539","orcid":"https://orcid.org/0000-0003-1296-4539","contributorId":198381,"corporation":false,"usgs":false,"family":"Blackwell","given":"Bradley","email":"","middleInitial":"D.","affiliations":[{"id":18090,"text":"U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL","active":true,"usgs":false}],"preferred":false,"id":829924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829925,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":202726,"corporation":false,"usgs":true,"family":"Gray","given":"James L.","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":829926,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lane, Rachael F. 0000-0001-9202-0612","orcid":"https://orcid.org/0000-0001-9202-0612","contributorId":222471,"corporation":false,"usgs":true,"family":"Lane","given":"Rachael","email":"","middleInitial":"F.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":829927,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":829928,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":829929,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829930,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sandstrom, Mark W. 0000-0003-0006-5675 sandstro@usgs.gov","orcid":"https://orcid.org/0000-0003-0006-5675","contributorId":706,"corporation":false,"usgs":true,"family":"Sandstrom","given":"Mark","email":"sandstro@usgs.gov","middleInitial":"W.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":829931,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829932,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Villeneuve, Daniel L. 0000-0003-2801-0203","orcid":"https://orcid.org/0000-0003-2801-0203","contributorId":219631,"corporation":false,"usgs":false,"family":"Villeneuve","given":"Daniel L.","affiliations":[{"id":39312,"text":"U.S. EPA","active":true,"usgs":false}],"preferred":false,"id":829933,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}}
,{"id":70227324,"text":"70227324 - 2022 - Acoustic and genetic data can reduce uncertainty regarding populations of migratory tree-roosting bats impacted by wind energy","interactions":[],"lastModifiedDate":"2022-01-10T13:10:25.611928","indexId":"70227324","displayToPublicDate":"2021-12-30T07:06:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5762,"text":"Animals","active":true,"publicationSubtype":{"id":10}},"title":"Acoustic and genetic data can reduce uncertainty regarding populations of migratory tree-roosting bats impacted by wind energy","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">Wind turbine-related mortality may pose a population-level threat for migratory tree-roosting bats, such as the hoary bat (<span class=\"html-italic\">Lasiurus cinereus</span>) in North America. These species are dispersed within their range, making it impractical to estimate census populations size using traditional survey methods. Nonetheless, understanding population size and trends is essential for evaluating and mitigating risk from wind turbine mortality. Using various sampling techniques, including systematic acoustic sampling and genetic analyses, we argue that building a weight of evidence regarding bat population status and trends is possible to (1) assess the sustainability of mortality associated with wind turbines; (2) determine the level of mitigation required; and (3) evaluate the effectiveness of mitigation measures to ensure population viability for these species. Long-term, systematic data collection remains the most viable option for reducing uncertainty regarding population trends for migratory tree-roosting bats. We recommend collecting acoustic data using the statistically robust North American Bat Monitoring Program (NABat) protocols and that genetic diversity is monitored at repeated time intervals to show species trends. There are no short-term actions to resolve these population-level questions; however, we discuss opportunities for relatively short-term investments that will lead to long-term success in reducing uncertainty.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/ani12010081","usgsCitation":"Hale, A., Hein, C.D., and Straw, B., 2022, Acoustic and genetic data can reduce uncertainty regarding populations of migratory tree-roosting bats impacted by wind energy: Animals, v. 12, no. 1, 81, 17 p., https://doi.org/10.3390/ani12010081.","productDescription":"81, 17 p.","ipdsId":"IP-133624","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":449335,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ani12010081","text":"Publisher Index Page"},{"id":394092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -130.78125,\n              48.22467264956519\n            ],\n            [\n              -123.04687499999999,\n              27.68352808378776\n            ],\n            [\n              -107.57812499999999,\n              15.284185114076433\n            ],\n            [\n              -78.3984375,\n              3.5134210456400448\n            ],\n            [\n              -75.5859375,\n              14.26438308756265\n            ],\n            [\n              -72.0703125,\n              22.917922936146045\n            ],\n            [\n              -70.3125,\n              36.31512514748051\n            ],\n            [\n              -49.5703125,\n              45.583289756006316\n            ],\n            [\n              -52.3828125,\n              59.355596110016315\n            ],\n            [\n              -94.21875,\n              70.37785394109224\n            ],\n            [\n              -145.1953125,\n              62.75472592723178\n            ],\n            [\n              -130.78125,\n              48.22467264956519\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hale, Amanda","contributorId":169317,"corporation":false,"usgs":false,"family":"Hale","given":"Amanda","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":830465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hein, Cris D.","contributorId":73910,"corporation":false,"usgs":false,"family":"Hein","given":"Cris","email":"","middleInitial":"D.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":830466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Straw, Bethany R. 0000-0001-9086-4600","orcid":"https://orcid.org/0000-0001-9086-4600","contributorId":271020,"corporation":false,"usgs":true,"family":"Straw","given":"Bethany","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830467,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70248809,"text":"70248809 - 2022 - Acquisition of Moon measurements by Earth orbiting sensors for lunar calibration","interactions":[],"lastModifiedDate":"2023-09-21T11:57:01.547194","indexId":"70248809","displayToPublicDate":"2021-12-30T06:55:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1944,"text":"IEEE Transactions on Geoscience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Acquisition of Moon measurements by Earth orbiting sensors for lunar calibration","docAbstract":"<div class=\"abstract-text row g-0\"><div class=\"col-12\"><div class=\"u-mb-1\"><div>The reflected light from the Moon can be utilized as a reference for radiometric calibration by employing a model to generate reference values corresponding to the Moon observations made by instruments. Using a calibration target that is outside the atmosphere provides a distinct advantage for space-based instruments; however, the lunar irradiance sensed by satellite instruments naturally changes as the host spacecraft traverses its orbit. This article presents a study of the potential impact on lunar radiometric measurements due to their acquisition from an orbiting platform. A simulation of a Sun-synchronous orbit was coupled to the U.S. Geological Survey (USGS) lunar model to generate predicted irradiances for points along orbit passes through several lunations. These irradiance values exhibit variations tied to the spacecraft motion, arising primarily from changes in the Moon-sensor distance and the phase angle. The two effects are similar in overall magnitude, but their respective contributions depend on the time of month and the orbit. Relative changes in irradiance mostly fall within an envelope of ±0.006% per second, except at the smallest phase angles. These studies enable planning space-based Moon observations to minimize the change in the target irradiance, an important consideration for measurements acquired for radiometric characterization of the Moon.</div></div></div></div>","language":"English","publisher":"IEEE","doi":"10.1109/TGRS.2021.3132590","usgsCitation":"Stone, T.C., 2022, Acquisition of Moon measurements by Earth orbiting sensors for lunar calibration: IEEE Transactions on Geoscience and Remote Sensing, v. 60, 1001706, 6 p., https://doi.org/10.1109/TGRS.2021.3132590.","productDescription":"1001706, 6 p.","ipdsId":"IP-132828","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":449338,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/tgrs.2021.3132590","text":"Publisher Index Page"},{"id":421016,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stone, Thomas C. 0000-0001-5088-3495 tstone@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3495","contributorId":242004,"corporation":false,"usgs":true,"family":"Stone","given":"Thomas","email":"tstone@usgs.gov","middleInitial":"C.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":883742,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70227325,"text":"70227325 - 2022 - Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional","interactions":[],"lastModifiedDate":"2022-03-28T16:37:58.702915","indexId":"70227325","displayToPublicDate":"2021-12-29T07:02:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of ‘Plant Functional Types’ (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, i.e. to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages (‘Lineage Functional Types’) will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16040","usgsCitation":"Anderegg, L.D., Griffith, D.M., Cavender-Bares, J., Riley, W.J., Berry, J.A., Dawson, T.E., and Still, C.J., 2022, Representing plant diversity in land models: An evolutionary approach to make ‘Functional Types’ more functional: Global Change Biology, v. 28, no. 8, p. 2541-2554, https://doi.org/10.1111/gcb.16040.","productDescription":"14 p.","startPage":"2541","endPage":"2554","ipdsId":"IP-114038","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":449340,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/3xc708ps","text":"External Repository"},{"id":394091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderegg, Leander D.L.","contributorId":256917,"corporation":false,"usgs":false,"family":"Anderegg","given":"Leander","email":"","middleInitial":"D.L.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":830468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Griffith, Daniel Mark 0000-0001-7463-4004","orcid":"https://orcid.org/0000-0001-7463-4004","contributorId":271033,"corporation":false,"usgs":true,"family":"Griffith","given":"Daniel","email":"","middleInitial":"Mark","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":830469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cavender-Bares, Jeannine","contributorId":219596,"corporation":false,"usgs":false,"family":"Cavender-Bares","given":"Jeannine","email":"","affiliations":[{"id":40035,"text":"U Minnesota","active":true,"usgs":false}],"preferred":false,"id":830470,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riley, William J. 0000-0002-4615-2304","orcid":"https://orcid.org/0000-0002-4615-2304","contributorId":194645,"corporation":false,"usgs":false,"family":"Riley","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":830471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berry, Joseph A.","contributorId":182349,"corporation":false,"usgs":false,"family":"Berry","given":"Joseph","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830472,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dawson, Todd E.","contributorId":176594,"corporation":false,"usgs":false,"family":"Dawson","given":"Todd","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":830473,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Still, Christopher J.","contributorId":167581,"corporation":false,"usgs":false,"family":"Still","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":24761,"text":"University of California, Santa Barbara; Oregon State University","active":true,"usgs":false}],"preferred":false,"id":830474,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70236051,"text":"70236051 - 2022 - Relational database for horizontal‐to‐vertical spectral ratios","interactions":[],"lastModifiedDate":"2022-08-26T12:01:07.832992","indexId":"70236051","displayToPublicDate":"2021-12-29T06:57:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Relational database for horizontal‐to‐vertical spectral ratios","docAbstract":"<p><span>Frequency‐dependent horizontal‐to‐vertical spectral ratios (HVSRs) of Fourier amplitudes from three‐component recordings can provide useful information for site response modeling. However, such information is not incorporated into most ground‐motion models, including those from Next‐Generation Attenuation projects, which instead use the time‐averaged shear‐wave velocity (</span><span class=\"inline-formula no-formula-id\">⁠<span id=\"MathJax-Element-1-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>V</mi><mi>S</mi></msub></math>\"><span id=\"MathJax-Span-1\" class=\"math\"><span><span id=\"MathJax-Span-2\" class=\"mrow\"><span id=\"MathJax-Span-3\" class=\"msub\"><span id=\"MathJax-Span-4\" class=\"mi\">V</span><span id=\"MathJax-Span-5\" class=\"mi\">S</span></span></span></span></span><span class=\"MJX_Assistive_MathML\">VS</span></span>⁠</span><span>) in the upper 30&nbsp;m of the site and sediment depth terms. To facilitate utilization of HVSR, we developed a publicly accessible relational database. This database is adapted from a similar repository for&nbsp;</span><span class=\"inline-formula no-formula-id\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><msub xmlns=&quot;&quot;><mi>V</mi><mi>S</mi></msub></math>\"><span id=\"MathJax-Span-6\" class=\"math\"><span><span id=\"MathJax-Span-7\" class=\"mrow\"><span id=\"MathJax-Span-8\" class=\"msub\"><span id=\"MathJax-Span-9\" class=\"mi\">V</span><span id=\"MathJax-Span-10\" class=\"mi\">S</span></span></span></span></span><span class=\"MJX_Assistive_MathML\">VS</span></span></span><span>&nbsp;data and provides microtremor‐based HVSR data (mHVSR) and supporting metadata, but not parameters derived from the data. Users can interact with the data directly within a web portal that contains a graphical user interface (GUI) or through external tools that perform cloud‐based computations. Within the database GUI, the median horizontal‐component mHVSR can be plotted against frequency, with the mean and mean ± one standard deviation (representing variability across time windows) provided. Using external interactive tools (provided as a Jupyter Notebook and an R script), users can replot mHVSR (as in the database) or create polar plots. These tools can also derive parameters of potential interest for modeling purposes, including a binary variable indicating whether an mHVSR plot contains peaks, as well as the fitted properties of those peaks (frequencies, amplitudes, and widths). Metadata are also accessible, which includes site location, details about the instruments used to make the measurements, and data processing information related to windowing, antitrigger routines, and filtering.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220210128","usgsCitation":"Wang, P., Zimmaro, P., Buckreis, T.E., Gospe, T., Brandenberg, S.J., Ahdi, S.K., Yong, A., and Stewart, J.P., 2022, Relational database for horizontal‐to‐vertical spectral ratios: Seismological Research Letters, v. 93, no. 2A, p. 1075-1088, https://doi.org/10.1785/0220210128.","productDescription":"14 p.","startPage":"1075","endPage":"1088","ipdsId":"IP-132531","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":405675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","issue":"2A","noUsgsAuthors":false,"publicationDate":"2021-12-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Pengfei","contributorId":217351,"corporation":false,"usgs":false,"family":"Wang","given":"Pengfei","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":849826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zimmaro, Paolo","contributorId":219068,"corporation":false,"usgs":false,"family":"Zimmaro","given":"Paolo","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":849827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buckreis, Tristan E","contributorId":295733,"corporation":false,"usgs":false,"family":"Buckreis","given":"Tristan","email":"","middleInitial":"E","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":849828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gospe, Tatiana","contributorId":265142,"corporation":false,"usgs":false,"family":"Gospe","given":"Tatiana","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":849829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandenberg, Scott J","contributorId":217350,"corporation":false,"usgs":false,"family":"Brandenberg","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":849830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahdi, Sean Kamran 0000-0003-0274-5180","orcid":"https://orcid.org/0000-0003-0274-5180","contributorId":265143,"corporation":false,"usgs":true,"family":"Ahdi","given":"Sean","email":"","middleInitial":"Kamran","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":849831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":849832,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":849833,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70256773,"text":"70256773 - 2022 - Reservoir attributes display cascading spatial patterns along river basins","interactions":[],"lastModifiedDate":"2024-09-06T15:46:21.690394","indexId":"70256773","displayToPublicDate":"2021-12-28T10:43:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Reservoir attributes display cascading spatial patterns along river basins","docAbstract":"<p><span>Considering reservoirs as linear fragments in a basin's river network could improve understanding, predictability, and management efficiency. We looked for general cascading spatial patterns across five categories of reservoir attributes: land cover, morphology and hydrology, fish habitat, fish assemblages, and fisheries. Attributes were pulled from various databases for large reservoirs (&gt;100&nbsp;ha) located in the United States. 16 widely distributed river basins, each including a minimum of 15 large reservoirs, were selected for analysis. Using analysis of covariance with basin as the class variable, we tested each attribute as a linear function of catchment area, which is an index of reservoir position in the basin. The majority of reservoir attributes displayed log-linear patterns as catchment area increased, indicating that reservoirs act as members of a larger network just as river reaches do. Several patterns were detected including attributes with no apparent lengthwise arrangement along the basin; cascading spatial patterns in which attributes increase or decrease from upstream to downstream within a basin; and attributes that increase with catchment area in some basins, decrease in others, or may simply remain constant throughout the basin. We conclude that each pattern may have different implications for management, and that the effectiveness with which most management activities influence reservoirs is likely to increase or decrease along river basins.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR029910","usgsCitation":"Faucheux, N., Sample, A., Aldridge, C., Norris, D., Owens, C., Starnes, V.R., VanderBloemen, S., and Miranda, L.E., 2022, Reservoir attributes display cascading spatial patterns along river basins: Water Resources Research, v. 58, no. 1, e2021WR029910, 14 p., https://doi.org/10.1029/2021WR029910.","productDescription":"e2021WR029910, 14 p.","ipdsId":"IP-119850","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Faucheux, N.M.","contributorId":341806,"corporation":false,"usgs":false,"family":"Faucheux","given":"N.M.","affiliations":[{"id":81792,"text":"Mississippi State Uni","active":true,"usgs":false}],"preferred":false,"id":908915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, A.R.","contributorId":341807,"corporation":false,"usgs":false,"family":"Sample","given":"A.R.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, C.A.","contributorId":275883,"corporation":false,"usgs":false,"family":"Aldridge","given":"C.A.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, D.M.","contributorId":341780,"corporation":false,"usgs":false,"family":"Norris","given":"D.M.","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":908918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Owens, C.","contributorId":341808,"corporation":false,"usgs":false,"family":"Owens","given":"C.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Starnes, Victoria R.","contributorId":343988,"corporation":false,"usgs":false,"family":"Starnes","given":"Victoria","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908920,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"VanderBloemen, S.","contributorId":341810,"corporation":false,"usgs":false,"family":"VanderBloemen","given":"S.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":908921,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908922,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70227360,"text":"70227360 - 2022 - Modeling the occurrence of M ∼ 5 caldera collapse-related earthquakes in Kīlauea volcano, Hawai'i","interactions":[],"lastModifiedDate":"2022-01-11T12:58:57.121497","indexId":"70227360","displayToPublicDate":"2021-12-28T06:56:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the occurrence of M ∼ 5 caldera collapse-related earthquakes in Kīlauea volcano, Hawai'i","docAbstract":"<div class=\"article-section__content en main\"><p>During the 2018 Kīlauea eruption and caldera collapse,<span>&nbsp;</span><i>M</i><span>&nbsp;</span>∼ 5 caldera collapse earthquakes occurred almost daily from mid-May until the beginning of August. While caldera collapses happen infrequently, the collapse-related seismicity damaged nearby structures, and so these events should be included in a complete seismic hazard assessment. Here, we present an approach to forecast the seismic hazard of the collapse earthquakes. We model their occurrence by combining a Poisson distribution for the number of collapses with a negative binomial for the number of earthquakes in a collapse, based on observations at Kīlauea. This rate model is then combined with a ground motion model to assess the seismic hazard posed by caldera collapse events. The rate model is non-Poisson but a Poisson model is adequate for low exceedance probabilities (e.g., &lt;10% in 50&nbsp;years). This approach could be generalized to model the hazard from earthquakes triggered by other underlying processes.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL092242","usgsCitation":"Llenos, A.L., and Michael, A.J., 2022, Modeling the occurrence of M ∼ 5 caldera collapse-related earthquakes in Kīlauea volcano, Hawai'i: Geophysical Research Letters, v. 49, no. 1, e2020GL092242, 9 p., https://doi.org/10.1029/2020GL092242.","productDescription":"e2020GL092242, 9 p.","ipdsId":"IP-130647","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":449344,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl092242","text":"Publisher Index Page"},{"id":394174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -155.41671752929688,\n              19.15165474470855\n            ],\n            [\n              -155.03082275390622,\n              19.15165474470855\n            ],\n            [\n              -155.03082275390622,\n              19.530024424775405\n            ],\n            [\n              -155.41671752929688,\n              19.530024424775405\n            ],\n            [\n              -155.41671752929688,\n              19.15165474470855\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Llenos, Andrea L. 0000-0002-4088-6737 allenos@usgs.gov","orcid":"https://orcid.org/0000-0002-4088-6737","contributorId":4455,"corporation":false,"usgs":true,"family":"Llenos","given":"Andrea","email":"allenos@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":830585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":830586,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227262,"text":"70227262 - 2022 - A quantitative soil-geomorphic framework for developing and mapping ecological site groups","interactions":[],"lastModifiedDate":"2022-01-05T12:54:42.95958","indexId":"70227262","displayToPublicDate":"2021-12-28T06:51:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"A quantitative soil-geomorphic framework for developing and mapping ecological site groups","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0001\" class=\"abstract author\"><div id=\"abss0001\"><p id=\"spara021\">Land management decisions need context about how landscapes will respond to different circumstances or actions. As ecologists’ understanding of nonlinear ecological dynamics has evolved into state-and-transition models (STMs), they have put more emphasis on defining and mapping the soil, geomorphological, and climate parameters that mediate these dynamics. The US Department of Agriculture Natural Resources Conservation Service ecological site descriptions (ESDs) have become the foremost system in classifying lands into ecological units based on STMs. However, an exhaustive inventory of ESDs has proved challenging to complete in the United States, and there have been questions about the consistency of detail in areas completed and the ability to objectively support some assertions made in existing ESDs. To address these issues, this study examines ESDs in the diverse Upper Colorado River region, where ESDs are only partially complete, to look at quantitative approaches to generalizing ecological site concepts based on unifying underlying soil, geomorphology, and climate patterns. Using existing ESDs and vegetation monitoring plot data, results show that a simple hierarchical soil geomorphic unit (SGU) framework based on topographic mediation of moisture, soil salinity, soil depth, slope, rock content, and soil texture can represent much of the ecological dynamics cataloged in ESDs. Analyses of reference plant production data, ecological state attribution, and regional monitoring data show that the new SGUs represent more variation than common climate parameters. This study also included predictively mapping SGUs at 30-m resolution (Kappa of 0.53, 74% agreement with top two predictions in validation). An optimized combination of SGUs with climate zones derived from an aridity index and maximum temperature of the hottest month resulted in an ecological site group framework that condensed over 826 unique ecological site records at various stages of completeness in the regional soil survey down to 35 intuitive and mappable ecological site groups.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2021.11.003","usgsCitation":"Nauman, T.W., Burch, S.S., Humphries, J.T., Knight, A.C., and Duniway, M.C., 2022, A quantitative soil-geomorphic framework for developing and mapping ecological site groups: Rangeland Ecology and Management, v. 81, p. 9-33, https://doi.org/10.1016/j.rama.2021.11.003.","productDescription":"25 p.","startPage":"9","endPage":"33","ipdsId":"IP-132575","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449346,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2021.11.003","text":"Publisher Index Page"},{"id":393902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burch, Samuel S 0000-0002-1142-7953","orcid":"https://orcid.org/0000-0002-1142-7953","contributorId":270936,"corporation":false,"usgs":true,"family":"Burch","given":"Samuel","email":"","middleInitial":"S","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Humphries, Joel T.","contributorId":270937,"corporation":false,"usgs":false,"family":"Humphries","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":56221,"text":"US Bureau of Land Management, Colorado State Office, Lakewood, CO 80215, USA","active":true,"usgs":false}],"preferred":false,"id":830166,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830167,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830168,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70227293,"text":"70227293 - 2022 - Demographic response of brown treesnakes to extended population suppression","interactions":[],"lastModifiedDate":"2022-02-15T16:21:59.502366","indexId":"70227293","displayToPublicDate":"2021-12-28T06:50:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Demographic response of brown treesnakes to extended population suppression","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>From a management perspective, reptiles are relatively novel invasive taxa. Few methods for reptile control have been developed and very little is known about their effectiveness for reducing reptile populations, particularly when the goal is eradication. Many reptiles, and especially snakes, are cryptic, secretive, and undergo extended periods of inactivity, traits that decrease detection probabilities and create challenges in estimating population size or evaluating management effects. The brown treesnake (<i>Boiga irregularis</i>) is a notorious invasive species that continues to cause major ecological and economic harm following their introduction to the island of Guam after World War II. They have been the subject of intensive research on the effectiveness of various techniques to control snakes, including the first ever aerial system for the distribution of toxic acetaminophen baits for reptile control. We provide a cohort-based life table for a cryptic and invasive reptile undergoing extended population control using toxic baits from March 2017–2020. We also evaluated the effects of single (toxic bait) versus multi-tool (toxic bait and live trapping) management efforts on population trajectories, and estimated which population vital rates are most important for influencing population growth or decline in a treated landscape. Treatment of the population with acetaminophen-laced baits resulted in an immediate reduction followed by a gradual population decline that suggested that eradication was the probable outcome given sufficient treatment time but that the period of treatment was decades in magnitude. Inclusion of live trapping reduced the predicted time required to achieve eradication by more than half. Preventing the transition of 1,000-mm snout-vent length (SVL) females to larger sizes was predicted to have the greatest effect on population reduction based on integral projection modeling. Our results suggest that toxic baits are capable of eradicating brown treesnakes in an enclosure, although inclusion of trapping reduced overall treatment time required. Tools that effectively target females &gt;1,000 mm SVL may have the greatest effect on reducing overall treatment timelines.</p></div></div>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22136","usgsCitation":"Nafus, M.G., Siers, S.R., Levine, B.A., Quiogue, Z.C., and Yackel Adams, A.A., 2022, Demographic response of brown treesnakes to extended population suppression: Journal of Wildlife Management, v. 86, no. 1, e22136, 19 p., https://doi.org/10.1002/jwmg.22136.","productDescription":"e22136, 19 p.","ipdsId":"IP-120666","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":449349,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22136","text":"Publisher Index Page"},{"id":436022,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NUZCGX","text":"USGS data release","linkHelpText":"Demographic data for toxicant based trial eradication of brown treesnakes in the USGS Closed Population on Guam, 2016 - 2020"},{"id":394009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              144.96734619140625,\n              13.605947651142655\n            ],\n            [\n              144.85061645507812,\n              13.663335011040553\n            ],\n            [\n              144.69680786132812,\n              13.507155459536346\n            ],\n            [\n              144.57870483398438,\n              13.445723447606865\n            ],\n            [\n              144.68032836914062,\n              13.219892851041191\n            ],\n            [\n              144.72976684570312,\n              13.21855594917547\n            ],\n            [\n              144.78057861328125,\n              13.318803207592538\n            ],\n            [\n              144.8011779785156,\n              13.417673157887597\n            ],\n            [\n              144.93850708007812,\n              13.516502424147102\n            ],\n            [\n              144.96734619140625,\n              13.605947651142655\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"86","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Nafus, Melia G. 0000-0002-7325-3055 mnafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":197462,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia","email":"mnafus@usgs.gov","middleInitial":"G.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":830327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levine, Brenna A.","contributorId":270994,"corporation":false,"usgs":false,"family":"Levine","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830328,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quiogue, Zachary C.","contributorId":270995,"corporation":false,"usgs":false,"family":"Quiogue","given":"Zachary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":830329,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830330,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70246520,"text":"70246520 - 2022 - Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","interactions":[],"lastModifiedDate":"2023-07-07T12:17:22.507283","indexId":"70246520","displayToPublicDate":"2021-12-27T07:13:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-gulliver text-s\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0040\">The uranium (U) content, and more recently, the ratio between<span>&nbsp;</span><sup>238</sup>U and<span>&nbsp;</span><sup>235</sup><span>U in&nbsp;black shales&nbsp;are commonly applied as a proxy to determine&nbsp;redox conditions&nbsp;and infer organic-richness. Uranium contents typically display a linear relationship with&nbsp;total organic carbon&nbsp;(TOC) in shales. This relationship is due to the processes and mechanisms responsible for the incorporation of U into the sediment during the deposition and&nbsp;remineralization&nbsp;of organic matter. This U/TOC relationship can vary, however, and some shales display uncharacteristically low U content despite having high TOC content, while others show large enrichments of U relative to TOC. Here we examine the U to TOC ratios and U-isotope compositions of three Upper Devonian-Lower Mississippian shales: the Woodford Shale, the Cleveland Shale, and the Bakken Shale, with two study sites in Oklahoma, one site in eastern Kentucky, and three sites in eastern Montana and western North Dakota, respectively. The U/TOC ratios of each shale are distinct from one another exhibiting average ratios ranging from 3 in the Cleveland Shale, to over 10 in the Bakken Shale. The distinct geochemical composition of the three shales suggests that, although lithologically similar, each study site represents a markedly different and dynamic&nbsp;depositional environment. The low average U/TOC (~3) along with the relatively high δ</span><sup>238</sup><span>U values (~0.03‰) of the Cleveland Shale core suggests deposition along the basin margin under normal marine conditions with periods of reduced bottom water&nbsp;oxygenation, likely due to fluctuations in the location of the&nbsp;pycnocline. The Woodford Shale on the other hand, shows higher U/TOC ratios (~4, George core, ~9, Poe core) and δ</span><sup>238</sup>U (~0.02‰ average, George core, ~0.06‰ average, Poe core), which suggests an unrestricted setting with intermittent euxinic conditions. In contrast, high U/TOC ratios (2–15), and very high δ<sup>238</sup><span>U values (up to 0.55‰) in the Bakken Shale cores indicate intense metal draw-down into sediments under sulfidic waters. The results show that when the U/TOC ratios and U-isotopic compositions of each studied shale are compared to modern anoxic basins and upwelling areas, it allows for an enhanced understanding of the paleoenvironmental conditions such as basin restriction and redox state of waters within the Late&nbsp;Devonian&nbsp;epicontinental seas&nbsp;of North America.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2021.110763","usgsCitation":"Abshire, M.L., Riedinger, N., Clymer, J.M., Scott, C., Severmann, S., Romaniello, S.J., and Puckette, J.O., 2022, Reconstructing the paleoceanographic and redox conditions responsible for variations in uranium content in North American Devonian black shales: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 587, 110763, 11 p., https://doi.org/10.1016/j.palaeo.2021.110763.","productDescription":"110763, 11 p.","ipdsId":"IP-126011","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449352,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.palaeo.2021.110763","text":"Publisher Index Page"},{"id":418743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"587","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Abshire, Michelle L.","contributorId":316208,"corporation":false,"usgs":false,"family":"Abshire","given":"Michelle","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":877030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riedinger, Natascha","contributorId":316209,"corporation":false,"usgs":false,"family":"Riedinger","given":"Natascha","email":"","affiliations":[],"preferred":false,"id":877031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clymer, John M.","contributorId":316210,"corporation":false,"usgs":false,"family":"Clymer","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":877032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":877033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Severmann, Silke","contributorId":316211,"corporation":false,"usgs":false,"family":"Severmann","given":"Silke","email":"","affiliations":[],"preferred":false,"id":877034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Romaniello, Stephen J.","contributorId":316212,"corporation":false,"usgs":false,"family":"Romaniello","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":877035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Puckette, James O.","contributorId":316213,"corporation":false,"usgs":false,"family":"Puckette","given":"James","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":877036,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70240811,"text":"70240811 - 2022 - Synthetic evaluation of infrasonic multipole waveform inversion","interactions":[],"lastModifiedDate":"2023-02-23T12:54:15.288009","indexId":"70240811","displayToPublicDate":"2021-12-27T06:51:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6006,"text":"Journal of Geophysical Research-Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Synthetic evaluation of infrasonic multipole waveform inversion","docAbstract":"<div class=\"article-section__content en main\"><p>Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3-D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal-to-noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill-posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies can help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023223","usgsCitation":"Iezzi, A., Matoza, R.S., Fee, D., Kim, K., and Jolly, A., 2022, Synthetic evaluation of infrasonic multipole waveform inversion: Journal of Geophysical Research-Solid Earth, v. 127, no. 1, e2021JB023223, 30 p., https://doi.org/10.1029/2021JB023223.","productDescription":"e2021JB023223, 30 p.","ipdsId":"IP-135441","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449354,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/1kr0z5t2","text":"External Repository"},{"id":413339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":864900,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matoza, Robin S","contributorId":215528,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":864901,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":864902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kim, Keehoon","contributorId":252842,"corporation":false,"usgs":false,"family":"Kim","given":"Keehoon","email":"","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":864903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jolly, A.D. 0000-0003-1020-9062","orcid":"https://orcid.org/0000-0003-1020-9062","contributorId":296487,"corporation":false,"usgs":true,"family":"Jolly","given":"A.D.","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":864904,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70227185,"text":"70227185 - 2022 - Geochemical insights into formation of enigmatic ironstones from Rio Grande rise, South Atlantic Ocean","interactions":[],"lastModifiedDate":"2022-01-04T15:43:27.547254","indexId":"70227185","displayToPublicDate":"2021-12-25T09:29:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical insights into formation of enigmatic ironstones from Rio Grande rise, South Atlantic Ocean","docAbstract":"<p><span>Rio Grande Rise (RGR) is an intraplate oceanic elevation in the South Atlantic Ocean that formed at a hotspot on the Mid-Atlantic Ridge during the Cretaceous. In spreading center and hotspot environments, ironstones form mainly by biomineralization of reduced Fe from hydrothermal fluids or oxidation of sulfide deposits. However, RGR has been considered aseismic and volcanically inactive for the past 46&nbsp;Ma. Here, we investigate the origin of ironstones collected from the summit of RGR using multiple techniques: petrographic observations, X-ray diffraction, U-Th/He geochronology, and chemical composition. The ironstones from RGR consist of finely laminated goethite containing igneous rock fragments, carbonate fluorapatite, and calcite. Our results suggest that Fe oxyhydroxides were precipitated by Fe-oxidizing bacteria forming bacterial mats. The bacterial Fe mats underwent compaction, dewatering, goethite crystallization, and cementation that created the ironstone deposits. U-Th/He geochronology reveals protracted goethite minimum ages extending from the late Miocene to the Quaternary, probably due to multiple generations of mats, slow mineralization rates, and Fe-oxide dissolution-reprecipitation cycles. Flame-like goethite structures underneath FeMn crusts and a chimney-shaped goethite sample with a central channel indicate that the dewatering fluid flowed upward through the deposits, or a thermal fluid source may have been introduced from below the ironstone deposits. High Fe/Mn ratios, low trace metals contents (Ni&nbsp;+&nbsp;Co&nbsp;+&nbsp;Cu), and very low Fe/REY ratios suggest ironstone precipitation from a hydrothermal fluid; however, REY</span><sub>SN</sub><span>&nbsp;plots and bivariate Ce</span><sub>SN</sub><span>&nbsp;/Ce</span><sub>SN</sub><sup>⁎</sup><span>&nbsp;versus Y</span><sub>SN</sub><span>/Ho</span><sub>SN</sub><span>&nbsp;and Ce</span><sub>SN</sub><span>&nbsp;/Ce</span><sub>SN</sub><sup>⁎</sup><span>&nbsp;versus Nd plots are inconclusive, and a proximal source of magma was unlikely during the period of mat formation. Given this evidence, we hypothesize that a geothermal circulation system may have facilitated ironstone mineralization at RGR.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2021.106716","usgsCitation":"Benites, M., Hein, J.R., Mizell, K., Farley, K.A., Treffkorn, J., and Jovane, L., 2022, Geochemical insights into formation of enigmatic ironstones from Rio Grande rise, South Atlantic Ocean: Marine Geology, v. 444, 106716, 18 p., https://doi.org/10.1016/j.margeo.2021.106716.","productDescription":"106716, 18 p.","ipdsId":"IP-135576","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":489032,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://dx.doi.org/10.1016/j.margeo.2021.106716","text":"External Repository"},{"id":393858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Rio Grande rise, South Atlantic Ocean","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -40,\n              -35\n            ],\n            [\n              -30,\n              -35\n            ],\n            [\n              -30,\n              -29\n            ],\n            [\n              -40,\n              -29\n            ],\n            [\n              -40,\n              -35\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"444","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benites, Mariana","contributorId":259240,"corporation":false,"usgs":false,"family":"Benites","given":"Mariana","email":"","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":829998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hein, James R. 0000-0002-5321-899X jhein@usgs.gov","orcid":"https://orcid.org/0000-0002-5321-899X","contributorId":140835,"corporation":false,"usgs":true,"family":"Hein","given":"James","email":"jhein@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":829999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mizell, Kira 0000-0002-5066-787X kmizell@usgs.gov","orcid":"https://orcid.org/0000-0002-5066-787X","contributorId":4914,"corporation":false,"usgs":true,"family":"Mizell","given":"Kira","email":"kmizell@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":830000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farley, Kenneth A.","contributorId":204209,"corporation":false,"usgs":false,"family":"Farley","given":"Kenneth","email":"","middleInitial":"A.","affiliations":[{"id":36877,"text":"Cal Tech","active":true,"usgs":false}],"preferred":false,"id":830001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Treffkorn, Jonathon 0000-0002-4953-8245","orcid":"https://orcid.org/0000-0002-4953-8245","contributorId":270768,"corporation":false,"usgs":false,"family":"Treffkorn","given":"Jonathon","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":830002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jovane, Luigi 0000-0003-4348-4714","orcid":"https://orcid.org/0000-0003-4348-4714","contributorId":259243,"corporation":false,"usgs":false,"family":"Jovane","given":"Luigi","email":"","affiliations":[{"id":48623,"text":"University of Sao Paulo","active":true,"usgs":false}],"preferred":false,"id":830003,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70229818,"text":"70229818 - 2022 - Effects of salinity and a glucocorticoid antagonist, RU486, on waterborne aldosterone and corticosterone of northern leopard frog larvae","interactions":[],"lastModifiedDate":"2022-03-18T14:20:11.565813","indexId":"70229818","displayToPublicDate":"2021-12-24T09:17:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of salinity and a glucocorticoid antagonist, RU486, on waterborne aldosterone and corticosterone of northern leopard frog larvae","docAbstract":"<p><span>Increased salinity is an emerging contaminant of concern for aquatic taxa. For amphibians exposed to salinity, there is scarce information about the physiological effects and changes in osmoregulatory hormones such as corticosterone (CORT) and aldosterone (ALDO). Recent studies have quantified effects of salinity on CORT physiology of amphibians based on waterborne hormone collection methods, but much less is known about ALDO in iono- and osmoregulation of amphibians. We re-assayed waterborne hormone samples from a previous study to investigate effects of salinity (sodium chloride, NaCl) and a glucocorticoid receptor antagonist (RU486) on ALDO of northern leopard frog (</span><i>Rana pipiens)</i><span>&nbsp;larvae. We also investigated relationships between ALDO and CORT. Waterborne ALDO marginally decreased with increasing salinity and was, unexpectedly, positively correlated with baseline and stress-induced waterborne CORT. Importantly, ALDO increased when larvae were exposed to RU486, suggesting that RU486 may also suppress mineralocorticoid receptors or that negative feedback of ALDO is mediated through glucocorticoid receptors. Alternatively, CORT increases with RU486 treatment and might be a substrate for ALDO synthesis, which could account for increases in ALDO with RU486 treatment and the correlation between CORT and ALDO. ALDO was negatively correlated with percent water, such that larvae secreting more ALDO retained less water. Although sample sizes were limited and further validation and studies are warranted, our findings expand our understanding of adrenal steroid responses to salinization in amphibians and proposes new hypotheses regarding the co-regulation of ALDO and CORT.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ygcen.2021.113972","usgsCitation":"Tornabene, B., Breuner, C., Hossack, B., and Crespi, E.J., 2022, Effects of salinity and a glucocorticoid antagonist, RU486, on waterborne aldosterone and corticosterone of northern leopard frog larvae: General and Comparative Endocrinology, v. 317, 113972, 6 p., https://doi.org/10.1016/j.ygcen.2021.113972.","productDescription":"113972, 6 p.","ipdsId":"IP-133771","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":449356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ygcen.2021.113972","text":"Publisher Index Page"},{"id":397304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"317","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":838469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breuner, Creagh W","contributorId":241893,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh W","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":838470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":838471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crespi, Erica J","contributorId":260876,"corporation":false,"usgs":false,"family":"Crespi","given":"Erica","email":"","middleInitial":"J","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":838472,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227511,"text":"70227511 - 2022 - Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","interactions":[],"lastModifiedDate":"2022-01-20T14:20:59.532904","indexId":"70227511","displayToPublicDate":"2021-12-24T08:15:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake","docAbstract":"<p><span>Cyanobacteria can produce numerous&nbsp;secondary metabolites&nbsp;(cyanotoxins) with various toxicities, yet data on cyanotoxins in many lakes are limited. Moreover, little research is available on complex relations among cyanobacteria that produce toxins. Therefore, we studied cyanobacteria and 19 cyanotoxins at three sites with recurring blooms in Kabetogama Lake (USA). Seven of 19 toxins were detected in various combinations. Anabaenopeptin A and B were detected in every sample. Microcystin-YR was detected more frequently than microcystin-LR, unlike other lakes in the region. Microcystin-YR concentrations, however, generally were low; two samples exceeded&nbsp;drinking water&nbsp;guidelines and no samples exceeded recreational guidelines. Anabaenopeptins correlated with six cyanobacterial taxa, most of which lack available literature on peptide production. The potential toxin producing cyanobacteria,&nbsp;</span><span><i>Microcystis</i></span><span>, was significantly correlated to microcystin-YR.&nbsp;</span><i>Pseudanabaena</i><span>&nbsp;sp. and&nbsp;</span><i>Synechococcus</i><span>&nbsp;sp. had strong negative correlations with several toxins that may indicate competition or stress between organisms. Non-metric multidimensional scaling identified three cyanobacterial pairs that may reflect symbiotic or antagonistic relations. This study highlights interactions among cyanobacteria and multiple cyanotoxins and the methods used may be useful for uncovering additional patterns in cyanobacteria communities in other systems, leading to further understanding of how those interactions lead to toxin production.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.128142","usgsCitation":"Christensen, V., Olds, H., Norland, J.E., and Khan, E., 2022, Phytoplankton community interactions and cyanotoxin mixtures in three recurring surface blooms within one lake: Journal of Hazardous Materials, v. 427, 128142, 12 p., https://doi.org/10.1016/j.jhazmat.2021.128142.","productDescription":"128142, 12 p.","ipdsId":"IP-128039","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":394575,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -93.11805725097656,\n              48.4105166936892\n            ],\n            [\n              -92.779541015625,\n              48.4105166936892\n            ],\n            [\n              -92.779541015625,\n              48.537977131982025\n            ],\n            [\n              -93.11805725097656,\n              48.537977131982025\n            ],\n            [\n              -93.11805725097656,\n              48.4105166936892\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"427","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831205,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":831206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":831207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":831208,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70230372,"text":"70230372 - 2022 - New insights into organic matter accumulation from high-resolution geochemical analysis of a black shale: Middle and Upper Devonian Horn River Group, Canada","interactions":[],"lastModifiedDate":"2022-07-07T16:47:14.960578","indexId":"70230372","displayToPublicDate":"2021-12-24T08:11:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"New insights into organic matter accumulation from high-resolution geochemical analysis of a black shale: Middle and Upper Devonian Horn River Group, Canada","docAbstract":"<p>Organic matter (OM) accumulation in organic matter-rich mudstones, or black shales, is generally recognized to be controlled by combinations of bioproductivity, preservation, and dilution. However, specific triggers of OM deposition in these formations are commonly difficult to identify with geochemical proxies, in part because of feedbacks that cause geochemical proxies for these controls to vary synchronously. This apparent synchronicity is partly a function of sample spacing, commonly at decimeter to meter intervals, which may represent longer periods of time than is required for the development of feedbacks. Higher resolution data sets may be required to fully interpret OM accumulation.</p><p>This study applies a novel combination of technologies to develop a high-resolution geochemical data set, integrating energy-dispersive X-ray fluorescence (EDXRF) and infrared imagery analyses, to record proxies for redox conditions, bioproductivity, and clastic and carbonate dilution in millimeter-resolution profiles of 133 core slabs from the Middle and Upper Devonian Horn River shale in the Western Canada Sedimentary Basin, which provides decadal-scale temporal resolution. A comparison to a more coarsely sampled data set from the same core results in substantially different interpretations of variations in bioproductivity, redox, and dilution proxies. Stratigraphic distributions of organic matter accumulation patterns (bioproductivity-control, siliciclastic/carbonate-dilution, and redox conditions-control) show that organic enrichment events were highly varied during deposition of the shale and were closely related to second- and third-order sea-level changes. High-resolution profiles indicate that bioproductivity was the predominant trigger for organic matter accumulation in a second-order highstand, particularly during deposition of third-order transgressive systems tracts. Organic matter accumulation was largely controlled by dilution from either carbonate or clastic sediments in a second-order lowstand. Bioproductivity-redox feedbacks developed on timescales of decades to centuries.</p>","language":"English","publisher":"Geological Society of America","doi":"10.1130/B36091.1","usgsCitation":"Zhou, H., Harris, N.B., Dong, T., Ayranci, K., Feng, J., Rivard, B., Hackley, P.C., and Hatcherian, J.J., 2022, New insights into organic matter accumulation from high-resolution geochemical analysis of a black shale: Middle and Upper Devonian Horn River Group, Canada: Geological Society of America Bulletin, v. 134, no. 7-8, p. 2130-2144, https://doi.org/10.1130/B36091.1.","productDescription":"15 p.","startPage":"2130","endPage":"2144","ipdsId":"IP-126339","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449362,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/gsab.s.17054183","text":"External Repository"},{"id":398462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"British Columbia, Northwest Territories","otherGeospatial":"Horn River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -125.91430664062499,\n              57.48040333923341\n            ],\n            [\n              -120.531005859375,\n              57.48040333923341\n            ],\n            [\n              -120.531005859375,\n              61.079544234557304\n            ],\n            [\n              -125.91430664062499,\n              61.079544234557304\n            ],\n            [\n              -125.91430664062499,\n              57.48040333923341\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"134","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2021-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Haolin","contributorId":289963,"corporation":false,"usgs":false,"family":"Zhou","given":"Haolin","email":"","affiliations":[],"preferred":false,"id":840106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Nicholas B.","contributorId":289966,"corporation":false,"usgs":false,"family":"Harris","given":"Nicholas","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":840107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dong, Tian","contributorId":239901,"corporation":false,"usgs":false,"family":"Dong","given":"Tian","email":"","affiliations":[{"id":48038,"text":"Institute for Geophysics and Department of Geological Sciences, Jackson School of Geosciences, University of Texas","active":true,"usgs":false}],"preferred":false,"id":840108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayranci, Korhan","contributorId":289969,"corporation":false,"usgs":false,"family":"Ayranci","given":"Korhan","email":"","affiliations":[],"preferred":false,"id":840109,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feng, Jilu","contributorId":289972,"corporation":false,"usgs":false,"family":"Feng","given":"Jilu","email":"","affiliations":[],"preferred":false,"id":840110,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rivard, Benoit","contributorId":289973,"corporation":false,"usgs":false,"family":"Rivard","given":"Benoit","email":"","affiliations":[],"preferred":false,"id":840111,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":840112,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":840113,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70227357,"text":"70227357 - 2022 - Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","interactions":[],"lastModifiedDate":"2022-05-13T14:36:19.096668","indexId":"70227357","displayToPublicDate":"2021-12-24T07:09:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10&nbsp;μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.</p></div></div>","language":"English","publisher":"National Ground Water Association","doi":"10.1111/gwat.13164","usgsCitation":"Knierim, K.J., Kingsbury, J.A., Belitz, K., Stackelberg, P.E., Minsley, B.J., and Rigby, J.R., 2022, Mapped predictions of manganese and arsenic in an alluvial aquifer using boosted regression trees: Groundwater, v. 60, no. 3, p. 362-376, https://doi.org/10.1111/gwat.13164.","productDescription":"15 p.","startPage":"362","endPage":"376","ipdsId":"IP-116535","costCenters":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":449364,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13164","text":"Publisher Index Page"},{"id":436023,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PRLNA3","text":"USGS data release","linkHelpText":"Machine-learning model predictions and rasters of arsenic and manganese in groundwater in the Mississippi River Valley alluvial aquifer"},{"id":394176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.93408203124999,\n              36.06686213257888\n            ],\n            [\n              -91.73583984374999,\n              35.0120020431607\n            ],\n            [\n              -92.30712890624999,\n              32.63937487360669\n            ],\n            [\n              -92.50488281249999,\n              30.50548389892728\n            ],\n            [\n              -91.73583984374999,\n              29.554345125748267\n            ],\n            [\n              -91.05468749999999,\n              29.05616970274342\n            ],\n            [\n              -89.38476562499999,\n              29.554345125748267\n            ],\n            [\n              -89.45068359374999,\n              30.543338954230222\n            ],\n            [\n              -89.93408203124999,\n              32.43561304116276\n            ],\n            [\n              -89.67041015624997,\n              33.94335994657882\n            ],\n            [\n              -89.20898437499999,\n              35.191766965947394\n            ],\n            [\n              -88.94531249999997,\n              36.08462129606931\n            ],\n            [\n              -89.27490234374999,\n              36.56260003738545\n            ],\n            [\n              -89.84619140624999,\n              36.27970720524017\n            ],\n            [\n              -89.93408203124999,\n              36.06686213257888\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"60","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":830569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":830571,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":260894,"corporation":false,"usgs":true,"family":"Rigby","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830572,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70256735,"text":"70256735 - 2022 - Does where they start affect where they finish? A multimethod investigation of the role of stocking location on survival and dispersal of hatchery-reared Lake Sturgeon in Missouri River tributaries","interactions":[],"lastModifiedDate":"2024-09-04T14:25:04.897104","indexId":"70256735","displayToPublicDate":"2021-12-23T09:17:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Does where they start affect where they finish? A multimethod investigation of the role of stocking location on survival and dispersal of hatchery-reared Lake Sturgeon in Missouri River tributaries","docAbstract":"<p><span>Despite ongoing Lake Sturgeon recovery efforts, little is known about the role of stocking location on survival and dispersal to nursery habitats. We stocked age-0 Lake Sturgeon at four sites in two adjacent Missouri River tributaries and used telemetry to examine whether survival and dispersal differed among stocking sites and rivers. Survival estimates from Barker Cormack-Jolly-Seber models that incorporated both receiver detections and auxiliary manual detections were higher than spatial capture-recapture models that only included receiver detections. Barker model overwinter survival averaged 53% and provided information to adjust individual censoring in spatial capture-recapture model dispersal estimates. Within the two rivers, stocking site had little effect on activity centers with individuals from both sites converging upon similar locations by the end of the study period. However, dispersal distance and direction differed among stocking locations. Our overwinter survival estimates of stocked age-0 Lake Sturgeon in Missouri River tributaries were equal to or higher than other studied populations suggesting stocked juveniles may be contributing to the recovering population. Tributaries were important overwintering nursery locations with high stream fidelity that may contribute to future homing among adults.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/rra.3925","usgsCitation":"Moore, M., Paukert, C.P., Bonnot, T., Brooke, B., and Moore, T., 2022, Does where they start affect where they finish? A multimethod investigation of the role of stocking location on survival and dispersal of hatchery-reared Lake Sturgeon in Missouri River tributaries: River Research and Applications, v. 38, no. 4, p. 627-638, https://doi.org/10.1002/rra.3925.","productDescription":"12 p.","startPage":"627","endPage":"638","ipdsId":"IP-124542","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Gasconade River, Missouri River, Osage River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -93,\n              39.25\n            ],\n            [\n              -93,\n              38\n            ],\n            [\n              -91,\n              38\n            ],\n            [\n              -91,\n              39.25\n            ],\n            [\n              -93,\n              39.25\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"38","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, M.J.","contributorId":341714,"corporation":false,"usgs":false,"family":"Moore","given":"M.J.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonnot, T.W.","contributorId":274985,"corporation":false,"usgs":false,"family":"Bonnot","given":"T.W.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brooke, B.","contributorId":341723,"corporation":false,"usgs":false,"family":"Brooke","given":"B.","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":908827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moore, T.","contributorId":257287,"corporation":false,"usgs":false,"family":"Moore","given":"T.","affiliations":[],"preferred":false,"id":908828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70254832,"text":"70254832 - 2022 - Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream","interactions":[],"lastModifiedDate":"2024-06-11T14:12:25.531179","indexId":"70254832","displayToPublicDate":"2021-12-23T09:03:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream","docAbstract":"<p><span>Research on fishes sometimes requires that individual fish be captured and subjected to invasive procedures multiple times over a relatively short time span. Electrofishing is one of the most common techniques used to capture fish, and it is known to cause injury to fish under certain circumstances. We evaluated the relationship of growth rates in Columbia River Redband Trout&nbsp;</span><i>Oncorhynchus mykiss gairdneri</i><span>&nbsp;to the number of times that they were captured via electrofishing and gastrically lavaged during the summer of 2018 in a mountainous, headwater stream. We captured fish between two and seven times over the course of 86 d using continuous (smooth) DC backpack electrofishing. We observed no relationship between the growth rate of Columbia River Redband Trout and the number of times that they were captured or gastrically lavaged. Although these findings contrast with hatchery electrofishing experiments, they may represent the greater resiliency of wild fish. It appears that researchers can use electrofishing and gastric lavage in cold waters at least once per month, and potentially up to twice per month, without greatly affecting the growth of wild Columbia River Redband Trout.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10728","usgsCitation":"Clancy, N.G., Dunnigan, J.L., and Budy, P., 2022, Relationship of trout growth to frequent electrofishing and diet collection in a headwater stream: North American Journal of Fisheries Management, v. 42, no. 1, p. 109-114, https://doi.org/10.1002/nafm.10728.","productDescription":"6 p.","startPage":"109","endPage":"114","ipdsId":"IP-133486","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bear Creek, Libby Creek, Ramsey Creek","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115.6766783082558,\n              48.4467644853234\n            ],\n            [\n              -115.6766783082558,\n              48.14538875631595\n            ],\n            [\n              -115.30322838074561,\n              48.14538875631595\n            ],\n            [\n              -115.30322838074561,\n              48.4467644853234\n            ],\n            [\n              -115.6766783082558,\n              48.4467644853234\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Clancy, Niall G.","contributorId":337769,"corporation":false,"usgs":false,"family":"Clancy","given":"Niall","email":"","middleInitial":"G.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunnigan, James L.","contributorId":337770,"corporation":false,"usgs":false,"family":"Dunnigan","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":52338,"text":"Montana Fish, Wildlife & Parks","active":true,"usgs":false}],"preferred":false,"id":902664,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227008,"text":"70227008 - 2022 - Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin","interactions":[],"lastModifiedDate":"2021-12-28T14:11:36.8632","indexId":"70227008","displayToPublicDate":"2021-12-23T08:30:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin","docAbstract":"<p><span>Freshwater scarcity has raised concerns about the long-term availability of the water supplies within the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin in Texas, New Mexico, and Chihuahua. Analysis of legacy temperature data and groundwater flux estimates indicates that the region’s known geothermal systems may contribute more than 45,000 tons of dissolved solids per year to the shallow aquifer system, with around 8500 tons of dissolved solids being delivered from localized groundwater upflow zones within those geothermal systems. If this salinity flux is steady and eventually flows into the Rio Grande, it could account for 22% of the typical average annual cumulative Rio Grande salinity that leaves the basin each year—this salinity proportion could be much greater in times of low streamflow. Regional water level mapping indicates upwelling brackish waters flow towards the Rio Grande and the southern part of the Mesilla portion of the basin with some water intercepted by wells in Las Cruces and northern Chihuahua. Upwelling waters ascend from depths greater than 1 km with focused flow along fault zones, uplifted bedrock, and/or fractured igneous intrusions. Overall, this work demonstrates the utility of using heat as a groundwater tracer to identify salinity sources and further informs stakeholders on the presence of several brackish upflow zones that could notably degrade the quality of international water supplies in this developed drought-stricken region.&nbsp;</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/w14010033","usgsCitation":"Pepin, J.D., Robertson, A.J., and Kelley, S.A., 2022, Salinity contributions from geothermal waters to the Rio Grande and shallow aquifer system in the transboundary Mesilla (United States)/Conejos-Médanos (Mexico) Basin: Water, v. 14, 33, 24 p., https://doi.org/10.3390/w14010033.","productDescription":"33, 24 p.","ipdsId":"IP-130212","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":449370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w14010033","text":"Publisher Index Page"},{"id":393412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Chuhuahua, New Mexico, Texas","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -107.60009765625,\n              30.372875188118016\n            ],\n            [\n              -105.6884765625,\n              30.372875188118016\n            ],\n            [\n              -105.6884765625,\n              32.9257074887604\n            ],\n            [\n              -107.60009765625,\n              32.9257074887604\n            ],\n            [\n              -107.60009765625,\n              30.372875188118016\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Jeffrey D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeffrey","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Andrew J. 0000-0003-2130-0347 ajrobert@usgs.gov","orcid":"https://orcid.org/0000-0003-2130-0347","contributorId":4129,"corporation":false,"usgs":true,"family":"Robertson","given":"Andrew","email":"ajrobert@usgs.gov","middleInitial":"J.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Shari A.","contributorId":216179,"corporation":false,"usgs":false,"family":"Kelley","given":"Shari","email":"","middleInitial":"A.","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":829165,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70227402,"text":"70227402 - 2022 - Improving groundwater model calibration with repeat microgravity measurements","interactions":[],"lastModifiedDate":"2022-05-13T14:37:28.20751","indexId":"70227402","displayToPublicDate":"2021-12-23T06:52:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Improving groundwater model calibration with repeat microgravity measurements","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using repeat microgravity—can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5&nbsp;m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13167","usgsCitation":"Kennedy, J.R., Wildermuth, L.M., Knight, J., and Larson, J., 2022, Improving groundwater model calibration with repeat microgravity measurements: Groundwater, v. 60, no. 3, p. 393-403, https://doi.org/10.1111/gwat.13167.","productDescription":"11 p.","startPage":"393","endPage":"403","ipdsId":"IP-126024","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436024,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9575C61","text":"USGS data release","linkHelpText":"MODFLOW-NWT groundwater model demonstrating groundwater model calibration with repeat microgravity measurements"},{"id":394305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.77392578125,\n              32.62087018318113\n            ],\n            [\n              -115.037841796875,\n              32.722598604044066\n            ],\n            [\n              -114.686279296875,\n              32.759562025650126\n            ],\n            [\n              -114.686279296875,\n              33.25706340236547\n            ],\n            [\n              -115.6640625,\n              33.25706340236547\n            ],\n            [\n              -115.77392578125,\n              32.62087018318113\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"60","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":830749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wildermuth, Libby M. 0000-0001-5333-0968 lwildermuth@usgs.gov","orcid":"https://orcid.org/0000-0001-5333-0968","contributorId":210459,"corporation":false,"usgs":true,"family":"Wildermuth","given":"Libby","email":"lwildermuth@usgs.gov","middleInitial":"M.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Jacob E. 0000-0003-0271-9011","orcid":"https://orcid.org/0000-0003-0271-9011","contributorId":204140,"corporation":false,"usgs":true,"family":"Knight","given":"Jacob E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Joshua D. 0000-0002-1218-800X","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":271085,"corporation":false,"usgs":true,"family":"Larson","given":"Joshua D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830752,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70232685,"text":"70232685 - 2022 - 2021 U.S. National Seismic Hazard Model for the State of Hawaii","interactions":[],"lastModifiedDate":"2022-07-12T13:21:48.024975","indexId":"70232685","displayToPublicDate":"2021-12-22T08:15:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"2021 U.S. National Seismic Hazard Model for the State of Hawaii","docAbstract":"The 2021 U.S. National Seismic Hazard Model (NSHM) for the State of Hawaii updates the two-decades-old former model by incorporating new data and modeling techniques to improve the underlying ground shaking forecasts of tectonic-fault, tectonic-flexure, volcanic, and caldera collapse earthquakes. Two earthquake ground shaking hazard models (public policy and research) are produced that differ in how they account for declustered catalogs. The earthquake source model is based on (1) declustered earthquake catalogs smoothed with adaptive smoothing methods, (2) earthquake rate forecasts based on three temporally varying 60-year time periods, (3) maximum magnitude models that extend to larger earthquakes than previously considered, (4) a separate Kīlauea-specific seismogenic caldera collapse model which accounts for clustered event behavior observed during the 2018 eruption, and (5) fault ruptures that consider historic seismicity, GPS-based strain rates, and a new Quaternary fault database. Two new Hawaii-specific ground motion models (GMMs) and five additional global models consistent with Hawaii shaking data are used to forecast ground shaking at 23 spectral periods and peak parameters. Site effects are modeled using western U.S. and Hawaii specific  empirical equations and provide shaking forecasts for eight site classes. For most sites the new model results in  similar spectral accelerations as those in the 2001 NSHM, with a few exceptions caused mostly by GMM changes. Ground motions are highest in the southern portion of the Island of Hawai‘i due to high rates of forecasted earthquakes on décollement faults. Shaking decays to the northwest where lower earthquake rates result from flexure of the tectonic plate. Large epistemic uncertainties in source characterizations and GMMs lead to an overall high uncertainty (more than a factor of 3) in ground shaking at Honolulu and Hilo. The new shaking model indicates significant chances of slight or greater damaging ground motions across most of the island chain.","language":"English","publisher":"SAGE Publishing","doi":"10.1177/87552930211052061","usgsCitation":"Petersen, M.D., Shumway, A., Powers, P.M., Moschetti, M.P., Llenos, A.L., Michael, A.J., Mueller, C., Frankel, A.D., Rezaeian, S., Rukstales, K., McNamara, D., Okubo, P., Zeng, Y., Jaiswal, K.S., Ahdi, S.K., Altekruse, J.M., and Shiro, B., 2022, 2021 U.S. National Seismic Hazard Model for the State of Hawaii: Earthquake Spectra, v. 38, no. 2, p. 865-916, https://doi.org/10.1177/87552930211052061.","productDescription":"52 p.","startPage":"865","endPage":"916","ipdsId":"IP-131306","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449374,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930211052061","text":"Publisher Index Page"},{"id":436025,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91V4SDT","text":"USGS data release","linkHelpText":"Data Release for the 2021 Update of the U.S. National Seismic Hazard Model for Hawaii"},{"id":403470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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